Financial networks are complex, opaque, and full of unknown information and variables. Large financial institutions have hundreds or even thousands of subsidiaries, issue hundreds or thousands of securities, and are tightly interconnected to thousands of institutions through millions of transactions and parallel relationships. Hedge funds, insurers and other financial institutions must navigate these interconnections on a daily basis, and problems in one institution can quickly transmit throughout the financial system, impacting any number of other institutions in their wake. Understanding and managing these interconnections is critical for many key functions in finance, including investment analysis, risk management, and financial supervision. For example, investment analysts need a clear, detailed understanding of securities issuers and guarantors to calculate values and expected returns on both equity and fixed income investments. Yet, institutions struggle to understand and analyze the complex web of relationships that are fundamental to their daily operations.
Financial analysis involves many diverse disciplines and requires data of various kinds, from a wide array of sources. The existing processes for gathering and structuring this information for use in analysis is manual, ad-hoc and frequently difficult to repeat or update. For example, when the data has already been structured it is typically stored in relational databases, spreadsheets, and other table-based systems. Unstructured data, such as that found in legal documents (SEC filings, court documents, etc.) is also frequently essential, and is even harder to gather, maintain and use. Existing analytical tools typically make the assumption that information regarding financial relationships is fundamentally hierarchical in nature, an assumption that is frequently and increasingly false. Additionally, existing analytical tools are poorly-suited to represent complex network structures that contain data at various levels of granularity, e.g. complex corporate ownership structures, debtor/creditor relationship networks, complex financial transactions that cross various kinds of boundaries, etc.
While relational databases are still the dominant technology used to store and query data used to analyze these kinds of situations, the rigidity of the table structures that must be used make analytics more difficult to implement and evolve. Object-oriented databases, which first emerged commercially in the 1990's, do not improve the situation in a substantive way. Rather, they are still based on rigid definitions of object structures, and are, if anything, even less convenient to query than relational databases. Querying relational databases and using full-text search are both well-understood techniques, but integrating the results of queries against multiple relational databases that do not share a common structure is time-consuming and difficult; adding data found from full-text search into the mix is done ad-hoc by individual analysts with varying degrees of success. In summary, many current tools and techniques lack both the necessary speed and flexibility to analyze and visualize networks of relationships in the financial world.